Abstract: A computerized method and system for evaluating customers of a financial institution using customer relationship value tags associated customer treatment actions includes automatically analyzing information about a customer from a database of the financial institution by a customer assessment engine using a predefined statistical model to assess the value of the customer to the financial institution. At least one customer treatment action associated with the assessed value is identified by the assessment engine, and the assessment engine marks a file associated with the customer with a mark representing the assessed value and the associated customer treatment action. The marked file is then accessed by other financial systems of the financial institution, or by customer representatives of the financial institution, and the associated customer treatment action is implemented by such systems or representatives in dealing with the customer.

Abstract: A technique is provided for determining credit ratings without the need for traditional credit bureau data. In at least one embodiment of the invention, a method comprises the steps of providing a network of linked identity records, each identity record comprising identity related information; receiving an identity record comprising identity related information of the entity; linking the received identity record with at least one of the identity records in the network of linked identity records, quantifying a plurality of linked identity records related to the entity; and generating a credit rating for the entity based on the quantified identity records. Application of the invention is particularly useful for individuals with less than a few years credit behavior recorded on a credit bureau, such as immigrants or young people with limited traditional historical information (“thin file”) and individuals with no traditional historical information (“no hit”).

Abstract: A computerized method and system for evaluating customers of a financial institution using customer relationship value tags associated customer treatment actions includes automatically analyzing information about a customer from a database of the financial institution by a customer assessment engine using a predefined statistical model to assess the value of the customer to the financial institution. At least one customer treatment action associated with the assessed value is identified by the assessment engine, and the assessment engine marks a file associated with the customer with a mark representing the assessed value and the associated customer treatment action. The marked file is then accessed by other financial systems of the financial institution, or by customer representatives of the financial institution, and the associated customer treatment action is implemented by such systems or representatives in dealing with the customer.

Abstract: A computerized method and system for evaluating customers of a financial institution using customer relationship value tags and associated customer treatment actions includes automatically analyzing information about a customer from a database of the financial institution by a customer assessment engine using a predefined statistical model to assess the value of the customer to the financial institution. At least one customer treatment action associated with the assessed value is identified by the assessment engine, and the assessment engine marks a file associated with the customer with a mark representing the assessed value and the associated customer treatment action. The marked file is then accessed by other financial systems of the financial institution, or by customer representatives of the financial institution, and the associated customer treatment action is implemented by such systems or representatives in dealing with the customer.

Abstract: A market selection optimization method comprising the steps of assigning product offers to consumers in the presence of multiple constraints, such that each consumer is assigned at least one product offer; determining a difference in expected profitability associated with the assigned product offer and a different product offer for each consumer; sorting the consumers according to the respective difference in expected profitabilities associated with the product offers; and reassigning the product offers to the sorted consumers in accordance with the respective difference in expected profitabilites. The result of this process is a selection of marketing offers that optimizes expected revenues.

Abstract: A market selection optimization method comprising the steps of assigning product offers to consumers in the presence of multiple constraints, such that each consumer is assigned at least one product offer; determining a difference in expected profitability associated with the assigned product offer and a different product offer for each consumer; sorting the consumers according to the respective difference in expected profitabilities associated with the product offers; and reassigning the product offers to the sorted consumers in accordance with the respective difference in expected profitabilites. The result of this process is a selection of marketing offers that optimizes expected revenues.

Abstract: A market selection optimization method comprising the steps of assigning product offers to consumers in the presence of multiple constraints, such that each consumer is assigned at least one product offer; determining a difference in expected profitability associated with the assigned product offer and a different product offer for each consumer; sorting the consumers according to the respective difference in expected profitabilities associated with the product offers; and reassigning the product offers to the sorted consumers in accordance with the respective difference in expected profitabilites. The result of this process is a selection of marketing offers that optimizes expected revenues.

Abstract: A market selection optimization method comprising the steps of assigning product offers to consumers in the presence of multiple constraints, such that each consumer is assigned at least one product offer; determining a difference in expected profitability associated with the assigned product offer and a different product offer for each consumer; sorting the consumers according to the respective difference in expected profitabilities associated with the product offers; and reassigning the product offers to the sorted consumers in accordance with the respective difference in expected profitabilites. The result of this process is a selection of marketing offers that optimizes expected revenues.

Abstract: A market selection optimization method comprising the steps of assigning product offers to consumers in the presence of multiple constraints, such that each consumer is assigned at least one product offer; determining a difference in expected profitabilty associated with the assigned product offer and a different product offer for each consumer; sorting the consumers according to the respective difference in expected profitabilities associated with the product offers; and reassigning the product offers to the sorted consumers in accordance with the respective difference in expected profitabilities. The result of this process is a selection of marketing offers that optimizes expected revenues.